Adding a base year index to R dataframe with multiple groups -

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i have yearly time series dataframe few grouping variables , need add index column based on particular year.

df <- data.frame(year = c(2000,2001,2002,2000,2001,2002),                   grp = c("a","a","a","b","b","b"),                  val = sample(6))

i want make simple index of variable val value divided value of base year, 2000:

df$val.ind <- df$val/df$val[df$year == 2000]

this not right not respect grouping variable grp. tried plyr not make work.

in actual problem have several grouping variables varying time series , i'm looking quite general solution.

we can create 'val.ind' after doing calculation within grouping variable ('grp'). can done in many ways.

one option data.table create 'data.table' 'data.frame' (setdt(df)), grouped 'grp', divide 'val' 'val' corresponds 'year' value of 2000.

 library(data.table)  setdt(df)[, val.ind := val/val[year==2000], = grp]

note: base year bit confusing wrt result. in example, both 'a' , 'b' grp have 'year' 2000. suppose, if op meant use minimum year value (considering numeric column), val/val[year==2000] in above code can replaced val/val[which.min(year)].

or can use similar code dplyr. group 'grp' , use mutate create 'val.ind'

 library(dplyr)  df %>%     group_by(grp) %>%     mutate(val.ind = val/val[year==2000])

here also, if needed replace val/val[year==2000] val/val[which.min(year)]

a base r option split/unsplit. split dataset 'grp' column convert data.frame list of dataframes, loop through list output lapply, create new column using transform (or within) , convert list added column single data.frame unsplit.

  unsplit(lapply(split(df, df$grp), function(x)            transform(x, val.ind= val/val[year==2000])), df$grp)

note can use do.call(rbind instead of unsplit. but, prefer unsplit same row order original dataset.


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